A deep learning library for streamlining research and development using the Torch7 distribution.
but havent tried it yet
I just tried the trick I mentioned above and it didn't seem to work
th> a = nn.Linear(2,2)
th> a.output:size()
[torch.LongStorage of size 0]
so I'm still learning some of the basics
havent had to debug much or reshape stuff until now
my output is a 3D tensor (batch size x sequence length x #classes), and my target is (batch size x sequence length)
and the module seems to read my ouput with the wrong view and cause an error
I can fix it using the output_module, but it also seems to narrow my target to only look at the first element in the sequence
local tgt = batch:targets():forward('b')
so I don't think the view I set is used right?
i remember having similar problems with Confusion; I think perhaps i was one-hot encoding when it was expecting class id's, or i was using class id's when it was expecting one-hot? I remember it took about 45 minutes to fix
to the network itself?
and are you running out of cpu memory or gpu memory?
Im guessing gpu?
Is the implementation of nn.SoftMaxTree() in nnx confirmed and known to be correct? I don't quite understand what's happening there. Firstly, nn.TreeNLLCriterion() does not return meaningful gradients(it distributes -1 along the batch size). This makes sense so long as the gradients do not influence nn.SoftMaxTree() gradients. However, I see that they do greatly influence what comes out of nn.SoftMaxTree()...
What's more, how does nn.SoftMaxTree() actually make a prediction? The only way to use it current (as far as I can tell) is to pass in a table of targets. However, when you are not training (i.e. when you are evaluating or actually using the model to predict), you obviously do not have targets.
Is it possible to produce a toy example that uses nn.SoftMaxTree() with the following characteristics: (1) it is wrapped in a sequencer, (2) training improvements are demonstrated over several epochs with batches of sequences, and (3) evaluation is demonstrated by generating a sequence after training
Such an example would go a long way to confirming that the implementation works, and would not take long to write for someone who is familiar with the module
Hi all. I need your help. I've run some experiments using the experiment wrapper in dp nn. Now, I want to load a saved experiment and run my test data tensors through the trained CNN model and capture the the outputs (features) in the last layer of the CNN, prior to the softmax. I would like to save these CNN feature vector tensors so that I can visualize their t-SNE embedding coordinates. Can anyone post some quick snippets of pseudo-code that will allow me to generate these feature vectors?
Hi, any help in how to nest a Sequencer module inside a Sequencer module [Nested Recurrence]? You have inner sequencer running over characters and extracting character features for every word. The outer sequencer runs over words. I am trying to implement this paper Multi-Task Cross Lingual Sequence tagging from Scratch
I have tried this model. But getting error during backpropagation
/usr/local/share/lua/5.1/nn/Linear.lua:99: invalid arguments: CudaTensor number CudaTensor CudaTensor
expected arguments: *CudaTensor~2D* [CudaTensor~2D] [float] CudaTensor~2D CudaTensor~2D | *CudaTensor~2D* float [CudaTensor~2D] float CudaTensor~2D CudaTensor~2D
stack traceback:
It comes out of
module:backward() and, tracing back, seems to imply that gradInput is of the wrong type. I'm using the stock callback function (mostly). Can anyone suggest how to track this down? I'm trying to avoid spending a day diving into the dirty bits of where dp, dpnn, and nn intersect.
@elbamos ouch. you can just go into a debugger and see at which layer this occurs and track the buffers coming from before and after are. I recommend mobdebug https://github.com/pkulchenko/MobDebug , really simple to use and set breakpoints.
